_{Publications:
Department for Computational Neuroscience}

Lloyd, K.; Sanborn, A.; Leslie, D.; Lewandowsky, S.: Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation. Cognitive Science 43 (12), pp. 1 - 43 (2019)

Wise, T.; Michely, J.; Dayan, P.; Dolan, R.: A computational account of threat-related attentional bias. PLoS Computational Biology 15 (10), pp. 1 - 21 (2019)

Zhao, S.; Chait, M.; Dick, F.; Dayan, P.; Furukawa, S.; Liao, H.: Pupil-linked phasic arousal evoked by violation but not emergence of regularity within rapid sound sequences. Nature Communications 10, 4030, pp. 1 - 16 (2019)

Javadi, A.; Patai, E.; Marin-Garcia, E.; Margolis, A.; Tan, H.-R.; Kumaran, D.; Nardini, M.; Duzel, E.; Dayan, P.; Spiers, H.: Prefrontal Dynamics Associated with Efficient Detours and Shortcuts: A Combined Functional Magnetic Resonance Imaging and Magnetoencenphalography Study. Journal of Cognitive Neuroscience 31 (8), pp. 1227 - 1247 (2019)

Javadi, A.-H.; Patai, E.; Marin-Garcia, E.; Margois, A.; Tan, H.-R.; Kumaran, D.; Nardini, M.; Penny, W.; Duzel, E.; Dayan, P. et al.; Spiers, H.: Backtracking during navigation is correlated with enhanced anterior cingulate activity and suppression of alpha oscillations and the "default-mode" network. Proceedings of the Royal Society B: Biological Sciences 286 (1908), 20191016, pp. 1 - 9 (2019)

Ahilan, S.; Solomon, R.; Breton, A.-Y.; Conover, K.; Niyogi, R.; Shizgal, P.; Dayan, P.: Learning to use past evidence in a sophisticated world model. PLoS Computational Biology 15 (6), pp. 1 - 20 (2019)

Dezfouli , A.; Griffiths, K.; Ramos, F.; Dayan, P.; Balleine, B.: Models that learn how humans learn: The case of decision-making and its disorders. PLoS Computational Biology 16 (6), pp. 1 - 33 (2019)

Rouault, M.; Dayan, P.; Fleming, S.: Forming global estimates of self-performance from local confidence. Nature Communications 10 (1), 1141, pp. 1 - 11 (2019)

Moran, R.; Keramati, M.; Dayan, P.; Dolan, R.: Retrospective model-based inference guides model-free credit assignment. Nature Communications 10, 750, pp. 1 - 14 (2019)

Jain, Y.; Gupta, S.; Rakesh, V.; Dayan, P.; Callaway, F.; Lieder, F.: How do people learn how to plan? In: Conference on Cognitive Computational Neuroscience (CCN 2019), PS-2A.70, pp. 826 - 829. Conference on Cognitive Computational Neuroscience (CCN 2019), Berlin, Germany, September 13, 2019 - September 16, 2019. (2019)

Ahilan, S.; Dayan, P.: Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. In: Annual Conference of the American Library Association (ALA 2019), 5, pp. 1 - 5. Annual Conference of the American Library Association (ALA 2019) , Washington, DC, USA, June 20, 2019 - June 25, 2019. (2019)

Ahilan, S.; Dayan, P.: Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. In: Workshop on Structure & Priors in Reinforcement Learning (SPiRL 2019) at ICLR 2019, pp. 1 - 11. Workshop on Structure & Priors in Reinforcement Learning (SPiRL 2019) at ICLR 2019, New Orleans, LA, USA, May 06, 2019. (2019)

Dayan, P.; Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dolan, R.: Anticipation, imagination and information seeking via mid-brain, hippocampal, and prefrontal interactions. In 49th Annual Meeting of the Society for Neuroscience (Neuroscience 2019), 018.12, pp. 103 - 104. 49th Annual Meeting of the Society for Neuroscience (Neuroscience 2019), Chicago, IL, USA, October 19, 2019 - October 23, 2019. (2019)

Mendl, M.; Jones, S.; Neville, V.; Higgs, L.; Robinson, E.; Dayan, P.; Paul, E.: An automated and self-initiated judgement bias task based on natural investigative behaviour. In Applied Ethology 2019: Animal lives worth living: 53rd Congress of the International Society of Applied Ethology (ISAE 2019), p. 126 (Eds. Newberry, R.; Braastad, B.). 53rd Congress of the International Society of Applied Ethology (ISAE 2019), Bergen, Norway, August 05, 2019 - August 09, 2019. Wageningen Academic Publishers, Wageningen, The Netherlands (2019)

Neville, V.; Paul, L.; Dayan, P.; Gilchrist, I.; Mendl, M.: Investigating animal affect and welfare using computational modelling. In Applied Ethology 2019: Animal lives worth living: 53rd Congress of the International Society of Applied Ethology (ISAE 2019), p. 127 (Eds. Newberry, R.; Braastad, B.). 53rd Congress of the International Society of Applied Ethology (ISAE 2019), Bergen, Norway, August 05, 2019 - August 09, 2019. Wageningen Academic Publishers, Wageningen, The Netherlands (2019)

Vilares, I.; Nolte, T.; Hula, A.; Cui, Z.; Fonagy, P.; Zhu, L.; Chiu, P.; King-Casas, B.; Lohrenz, T.; Dayan, P. et al.; Montague, R.: Computational Phenotyping in Borderline Personality Using a Role-Based Social Hierarchy Probe. In Biological Psychiatry, 85 (10 Supplement), O8, p. S108. 74th Annual Meeting of the Society of Biological Psychiatry, Chicago, IL, USA, May 16, 2019 - May 18, 2019. Elsevier, New York (2019)

Dayan, P.: Savouring and its Modulation by Prediction Errors. Central European University, Budapest, Hungary (2019)

Dayan, P.; Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dolan, R.: Savouring and its Modulation by Prediction Errors. Departmental Colloquium Max Planck for Biological Cybernetics, Tübingen, Germany (2019)

Dinkel, H.: Introduction of Linear motifs. EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Dinkel, H.; Gibson, T.; Kumar, M.: Linear motif biology and prediction, cooperativity in cellular signaling (I). EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Kumar, M.; Dinkel, H.; Gibson, T.: Linear motif biology and prediction, cooperativity in cellular signaling (II). EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Sharan, M.; Dinkel, H.: Using informal meetings for building local (bioinformatics) communities. EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Dayan, P.: Replay and Preplay in Human Planning. Université de Genève: C. Lüscher Synapses, Circuits and Behaviour in Addiction and Related Disorders, Genève, Switzerland (2019)

Dayan, P.: Neural Reinforcement Learning. Humboldt Lecture Series at Tübingen University , Tübingen, Germany (2019)

Dayan, P.; Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dolan, R.: Savouring and its Modulation by Prediction Errors. Colloquium Cognitive Systems: Universität Ulm , Ulm, Germany (2019)

Dayan, P.: Savouring and its Modulation by Prediction Errors. 17th Annual Meeting of the Society for NeuroEconomics (SNE 2019), Dublin, Ireland (2019)

Dayan, P.: Theoretical Neuroscience: Decision Making and its Discontents. 49th Annual Meeting of the Society for Neuroscience (Neuroscience 2019) , Chicago, IL, USA (2019)

Sharan, M.; Dinkel, H.: Building Blocks of Python Programming. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Sharan, M.; Dinkel, H.; Alves, R.: Building programs with Python I. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Sharan, M.; Dinkel, H.; Alves, R.: Building programs with Python II. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Sharan, M.; Dinkel, H.; Alves, R.: Using commandline git for version control. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Dayan, P.: The cortical dynamics of integrative decision making. 6th ESI Systems Neuroscience Conference 2019: The recurrent cortex: feedback, dynamics, and dimensionality , Frankfurt a.M., Germany (2019)

Dayan, P.: Unsupervised Yearning. David Marr, 50 years on (Marr 2019) at Artificial & Biological Cognition 2019, Cambridge, UK (2019)

Dayan, P.: Dysfunctional decision-making. Tübingen International Summer School (TISS 2019), Kloster Heiligkreuztal (2019)

Dayan, P.: Functional decision-making. Tübingen International Summer School (TISS 2019), Kloster Heiligkreuztal (2019)

Dayan, P.: Reinforcement learning. 2019 Computational Psychiatry Summer Course, New York, NY, USA (2019)

Dayan, P.: Theoretical approaches to function and dysfunction. 2019 Computational Psychiatry Summer Course, New York, NY, USA (2019)

Dayan, P.: Slothful serial: perilous parallel processing. Workshop Heuristics, Hacks and Habits: 41st Annual Meeting of the Cognitive Science Society (COGSCI 2019), Montréal, Canada (2019)

Dayan, P.: Decision-making and some of its Discontents. 9th IMPRS NeuroCom Summer School in Cognitive Neuroscience , Leipzig, Germany (2019)

Dayan, P.: Savouring and its Modulation by Prediction Errors. NEUREX Workshop: Neuroeconomics: Studying Decision Making in an Economic Context, Strasbourg, France (2019)

Dayan, P.: Pavlovian-Instrumental Interactions in Active Avoidance: The Bark of Neutral Trials. Modulation of Neural Circuits and Behavior Gordon Research Conference: Neuromodulatory Mechanisms Underlying Flexible Brain Functions and Behavior , Les Diablerets, Switzerland (2019)

Dayan, P.: The Cortical Dynamics of Integrative Decision Making. University of Freiburg: Bernstein Seminar, Freiburg i.Br., Germany (2019)

Dayan, P.: Learning in Natural Neural Systems. Eberhard-Karls-Universität Tübingen: Studium Generale: Maschinelles Lernen, Tübingen, Germany (2019)

Dayan, P.: The long and the short of serotonergic stimulation: The learning rate for rewards. NeuroRetreat 2019: Max Planck Institute of Psychiatry, München, Germany (2019)

Dayan, P.: The Good, The Bad, and Something Inbetween: Dopamine in Active Avoidance. Centro Champalimaud, Lisboa, Portugal (2019)

Dayan, P.: The long and the short of 5-HT stimulation: optogenetic activation of dorsal raphe serotonergic neurons changes the learning rate for rewards. COSYNE 2019 Workshop: Advances and convergences in 5-HT research , Cascais, Portugal (2019)

Dayan, P.: The Cortical Dynamics of Integrative Decision Making. Chen Institute Symposium 2019, Pasadena, CA, USA (2019)

Bröker, F.; Love, B.; Dayan, P.: Semi-Supervised Categorisation: Input Representations Determine Necessity of Feedback. 20th Conference of Junior Neuroscientists (NeNa 2019) , Schramberg, Germany (2019)

Bruijns, S.; Dayan, P.: Anticipating Every Turn: Learning About Learning in Mice. 20th Conference of Junior Neuroscientists (NeNa 2019) , Schramberg, Germany (2019)

Ahilan, S.; Dayan, P.: Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2019), Montreal, Canada (2019)

Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O’Doherty, J.; Dayan, P.; Dolan, R.: Hippocampal-midbrain circuit enhances the pleasure of anticipation in the prefrontal cortex. 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2019), Montreal, Canada (2019)

Zhao, S.; Dick, F.; Dayan, P.; Furukawa, S.; Liao, H.; chait, M.: Phasic norepinephrine is a neural interrupt signal for unexpected events in rapidly unfolding sensory sequences: evidence from pupillometry. International Conference on Interdisciplenary Advances in Statistical Learning 2019, San Sebastián, Spain (2019)

Zhao, S.; Dick, F.; Dayan, P.; Furukawa, S.; Liao, H.; Chait, M.: Phasic norepinephrine is a neural interrupt signal for unexpected events in rapidly unfolding sensory sequences: evidence from pupillometry. Interdisciplinary Advances in Statistical Learning 2019, San Sebastián, Spain (2019)

Stojic, H.; Orquin, J.; Dayan, P.; Dolan, R.; Speekenbrink, M.: Rewards and uncertainty jointly drive the attention dynamics in reinforcement learning. Ninth International Symposium on Biology of Decision Making (SBDM 2019) , Oxford, UK (2019)

Zamfir, E.; Dayan, P.: The dynamical interaction between attribution and belief: Evidence from a novel task. Ninth International Symposium on Biology of Decision Making (SBDM 2019), Oxford, UK (2019)

Zhao, S.; Dick, F.; Dayan, P.; Furukawa, S.; Liao, H.-I.; Chait, M.: Phasic norepinephrine is a neural interrupt signal for unexpected events in rapidly unfolding sensory sequences: evidence from pupillometry. Ninth International Symposium on Biology of Decision Making (SBDM 2019), Oxford, UK (2019)